AI Model fine tuning
Pre-trained models are just the starting point. We fine-tune them to fit your business logic, domain vocabulary, and workflow nuances. Whether it’s adapting open-source models like Llama or DeepSeek to your internal knowledge base, or optimizing lighweight models for cost-efficient inference, the goal is always the same — output that’s not just smart, but relevant. From structured prompt engineering to full adapter-based tuning, every model is aligned with your real-world needs.
AI implementation & integration
AI agents are built to fit seamlessly into your existing systems through reliable AI integration. Whether powered by n8n or custom-developed in your preferred programming language, each solution is implemented and integrated around your business processes. These agents go beyond simple responses — they retain context and evolve over time, with persistent memory supported by Redis, Supabase, or SQL. The result is an intelligent assistant that adapts, improves, and continues delivering value long after launch.
MCP (Model Context Protocol)
Context awareness is built in from the start. With MCP, each agent knows what it’s doing, why it’s doing it, and when to shift focus. Instead of losing track mid-task, it carries context across interactions and passes it to the model automatically. The outcome is consistency, accuracy, and natural, coherent communication — structured context, not fragmented replies.
Agentic RAG (Retreival Augmented Generation)
No one fine-tuns models just to have them go stale next month. Instead of locking knowledge into static weights, we implement Agentic RAG — where the agent dynamically retrieves the latest data, thinks through the task, and acts accordingly. New documents? Updated policies? No room for hallucinations? No problem. The agent knows how to fetch what it needs when it needs it. You stay current without retraining every time your content changes. That’s how real-time AI should work.
Private hosting
Deployment happens on your terms. Agents are privately hosted, tailored to your setup — not tied to someone else’s cloud. The agent runs where you need it, with the performance and privacy you expect, without burning your budget. No hidden platform fees. No lock-in. Just a system that works — fast, lean, and under your control.
Multi Agent Applications
We design multi-agent applications where intelligence is distributed, not centralized. In real-world use, multiple agents working together bring modularity, specialization, and scalability to a whole new level. Each system is structured around a Root Agent that coordinates and delegates tasks to specialized sub-agents — communication, analysis, automation, or decision-making — all working in sync. This modular architecture isn’t just efficient; it’s the foundation for building truly capable, adaptable, and complex AI ecosystems.
